Epigenetic biomarkers to predict psoriasis disease progression * towards tailored therapy.
- Conditions
- psoriasisskin disease with fast proliferation and less differentiation of skin cells1004078910011063
- Registration Number
- NL-OMON40335
- Lead Sponsor
- Maastricht Universitair Medisch Centrum +
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Completed
- Sex
- Not specified
- Target Recruitment
- 9
For psoriasis patients:
- Age: >18 years old
- No systemic therapy for psoriasis
- Clinical diagnosis: Psoriasis Vulgaris or Psoriasis Guttata (The diagnosis will be confirmed by a dermatologist)
- Topical therapy is OK if it IS possible to stop for 3 weeks;For the control group:
- Age: >18 years old
- No generalised skin disease
For psoriasis patients:
- Age: <18 years old
- Systemic therapy for psoriasis
- Juvenile psoriasis
- Not meeting the inclusion criteria
- Excessive scar formation or keloid in medical history
- Other (skin) disease that could influence the psoriasis
- Minors or incapacitated subjects
- Allergic reaction to lidocaine in medical history
- Topical therapy if it is NOT possible to stop for 3 weeks;For the control group:
- Clinical diagnosis of psoriasis
- Other generalised skin disease
- Excessive scar formation or keloid in medical history
- Minors or incapacitated subjects
- Allergic reaction to lidocaine in medical history
Study & Design
- Study Type
- Observational invasive
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method <p>The main study parameter is the sequence of DNA-methylation status (methylome)<br /><br>of the patient material. From these sequences a top set of genes (defined from<br /><br>differential methylation) will be defined that most reliably (r >=0.95,<br /><br>FDR<0.05) distinguish psoriasis from normal skin, indicating potential<br /><br>biomarkers that will be further studied in a future trial. </p><br>
- Secondary Outcome Measures
Name Time Method <p>Secondary study parameter is the full transcriptome of the patient material. We<br /><br>will use this data to determine whether the methylation patterns that we find<br /><br>affect gene expression. Biomarkers of necessity reflect disease processes that<br /><br>are correlated to a particular outcome. Thus, for an epigenetic marker, if it<br /><br>is to be relevant to the disease it must have consequences on gene expression.<br /><br>We will not analyse the data for other parameters such as disease-causing<br /><br>mutations.</p><br>